8,096 research outputs found
Linking Surface Facts to Large-Scale Knowledge Graphs
Open Information Extraction (OIE) methods extract facts from natural language
text in the form of ("subject"; "relation"; "object") triples. These facts are,
however, merely surface forms, the ambiguity of which impedes their downstream
usage; e.g., the surface phrase "Michael Jordan" may refer to either the former
basketball player or the university professor. Knowledge Graphs (KGs), on the
other hand, contain facts in a canonical (i.e., unambiguous) form, but their
coverage is limited by a static schema (i.e., a fixed set of entities and
predicates). To bridge this gap, we need the best of both worlds: (i) high
coverage of free-text OIEs, and (ii) semantic precision (i.e., monosemy) of
KGs. In order to achieve this goal, we propose a new benchmark with novel
evaluation protocols that can, for example, measure fact linking performance on
a granular triple slot level, while also measuring if a system has the ability
to recognize that a surface form has no match in the existing KG. Our extensive
evaluation of several baselines show that detection of out-of-KG entities and
predicates is more difficult than accurate linking to existing ones, thus
calling for more research efforts on this difficult task. We publicly release
all resources (data, benchmark and code) on
https://github.com/nec-research/fact-linking
A Survey on Knowledge Graphs: Representation, Acquisition and Applications
Human knowledge provides a formal understanding of the world. Knowledge
graphs that represent structural relations between entities have become an
increasingly popular research direction towards cognition and human-level
intelligence. In this survey, we provide a comprehensive review of knowledge
graph covering overall research topics about 1) knowledge graph representation
learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph,
and 4) knowledge-aware applications, and summarize recent breakthroughs and
perspective directions to facilitate future research. We propose a full-view
categorization and new taxonomies on these topics. Knowledge graph embedding is
organized from four aspects of representation space, scoring function, encoding
models, and auxiliary information. For knowledge acquisition, especially
knowledge graph completion, embedding methods, path inference, and logical rule
reasoning, are reviewed. We further explore several emerging topics, including
meta relational learning, commonsense reasoning, and temporal knowledge graphs.
To facilitate future research on knowledge graphs, we also provide a curated
collection of datasets and open-source libraries on different tasks. In the
end, we have a thorough outlook on several promising research directions
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